Abstract
This article explores the utilization of the processing power of GPUs using CUDA computation for real-time aggregation of multi-sensor data and detection of 3D objects using parallel clustering algorithms. The purpose is to implement an algorithm that fuses raw lidar point cloud data and 2D camera image object detections to produce 3D object clusters in a lidar point cloud. Most of the computation has been implemented using CUDA parallelism to investigate the capability of GPU devices in this task, which is a common challenge in automated driving. The results indicate that processing times can be optimized within the algorithm, which is crucial when considering the large amounts of data provided by lidar and camera-based systems. The algorithm can perform inference on the Jetson Xavier AGX at rates of ~20 to ~220 ms depending on the number of objects and their corresponding point amounts in the KITTI dataset.
Original language | English |
---|---|
Title of host publication | Proceedings - 2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing Conference, ICCP 2022 |
Editors | Sergiu Nedevschi, Rodica Potolea, Radu Razvan Slavescu |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Pages | 113-118 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-6654-6437-6 |
DOIs | |
Publication status | Published - 2022 |
MoE publication type | A4 Article in a conference publication |
Event | 2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing, ICCP 2022 - Cluj-Napoca, Romania Duration: 22 Sept 2022 → 24 Sept 2022 |
Conference
Conference | 2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing, ICCP 2022 |
---|---|
Country/Territory | Romania |
City | Cluj-Napoca |
Period | 22/09/22 → 24/09/22 |
Keywords
- 3D object detection
- automated driving
- CUDA
- sensor data fusion